What statistical tests does the Research & Analysis skill support?

The skill supports a wide array of tests including Independent t-tests, ANOVA, Mann-Whitney U, Kruskal-Wallis, Pearson/Spearman correlation, and Chi-squared tests, automatically selecting the best fit based on your data distribution.

Can I use this skill to analyze my own CSV or JSON files?

Yes, the skill is designed to load and process external datasets provided in common formats like CSV and JSON, as well as internal SuperClaw metrics.

Where are the generated charts and plots saved?

All visualizations are saved as high-resolution images in the ~/superclaw/data/analysis/ directory with descriptive filenames for easy retrieval and reporting.

Does it provide more than just a p-value?

Absolutely. Following scientific best practices, the skill reports effect sizes, confidence intervals, and test statistics, while also documenting any assumption violations or data limitations.

Research & Statistical Analysis

Name: Research & Statistical Analysis
Author: jaminitachi

byjaminitachi

0•

Data Science & ML

Performs hypothesis-driven statistical analysis and data visualization on datasets, system metrics, and experiment logs.

The Research & Statistical Analysis skill brings scientific rigor to your development workflow by automating complex data interpretation tasks. It bridges the gap between raw data and actionable insights by performing exploratory data analysis, selecting appropriate statistical tests—such as t-tests, ANOVA, or Mann-Whitney U—and generating publication-quality visualizations. Whether you are correlating system heartbeat metrics, comparing experiment outcomes, or analyzing external CSV/JSON datasets, this skill ensures results are backed by p-values, effect sizes, and documented limitations rather than simple intuition.

Key Features

01Publication-quality visualization generation using Matplotlib and Seaborn

02Hypothesis-driven statistical testing including T-tests, ANOVA, and non-parametric alternatives

03Standardized reporting with effect sizes, confidence intervals, and limitation tracking

04Automated data exploration with descriptive statistics and distribution analysis

05Seamless integration with SuperClaw heartbeat metrics and experiment-log data

060 GitHub stars

Use Cases

01Correlating system performance metrics like CPU usage with API response times to identify bottlenecks

02Comparing performance results between multiple experiment groups to determine statistical significance

03Analyzing longitudinal trends in application logs or user behavior datasets to guide product decisions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jaminitachi/superclaw research-analysis

For use in Claude.ai and ChatGPT

Download Skill